What are materialized views in relational databases?

Materialized views are database objects that store the results of a query physically, like a table, and can be refreshed to update the data. Unlike standard views, which are virtual and recalculate results each time they’re queried, materialized views persist the data to disk. This makes them useful for optimizing complex or resource-intensive queries that are executed frequently. For example, a materialized view might precompute the total sales per region from a large sales table, allowing users to retrieve the aggregated data quickly without reprocessing millions of rows every time.

The key mechanism behind materialized views is their ability to cache query results. When you create a materialized view, the database runs the underlying query once and saves the output. This stored data can then be accessed directly, reducing computational overhead. However, the data in the view becomes stale if the source tables change, so databases provide refresh options. For instance, PostgreSQL allows manual refreshes (REFRESH MATERIALIZED VIEW) or automated refreshes using triggers or scheduled jobs. In Oracle, you can configure materialized views to refresh incrementally (using log-based changes) or fully (rebuilding the entire dataset). For example, a daily sales summary materialized view might refresh every night to balance performance and data freshness.

Materialized views are particularly valuable in scenarios where read performance is critical, such as reporting or data warehousing. They trade off storage space and slightly delayed data for faster query responses. However, they aren’t ideal for transactional systems requiring real-time accuracy. For instance, an e-commerce dashboard showing hourly sales trends could use a materialized view to avoid repeatedly aggregating raw order data. Developers should also consider the overhead: frequent refreshes can strain the database, while infrequent refreshes risk outdated results. Additionally, materialized views often require indexes (like those on tables) to optimize access, adding maintenance complexity. Proper use cases depend on balancing these trade-offs for specific workloads.